***NEW! Specialization Completion Challenge, receive Qwiklabs credits valued up to $150! See below for details.***
What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models.
Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.
SPECIALIZATION COMPLETION CHALLENGE
As if learning new skills wasn’t enough of an incentive, we're excited to announce a special completion challenge for 'Machine Learning with TensorFlow on Google Cloud Platform’ specialization.
Here’s how it works: Our completion challenge runs through 11:59pm PT May 5, 2019. Complete any course in this Specialization including this one, anytime in this period and we'll send you 30 Qwiklabs credits for each course completed (upto $150 value given there are 5 courses in the specialization).
You can use these credits to take additional labs and earn badges, which you can then add to your resume and social profiles.
Your challenge awaits – begin learning on Coursera today!
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From the lesson

Inclusive ML

This module will discuss why machine learning systems aren’t fair by default and some of the things you have to keep in mind as you infuse ML into your products.